This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
Este curso faz parte do Programa de cursos integrados Ciência de dados aplicada com Python
610.663 já se inscreveram
Instrutor(es): Christopher Brooks 
Legendas: Árabe, Francês,
oferecido por
Programa de cursos integrados Ciência de dados aplicada com PythonUniversidade de MichiganInformações sobre o curso
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Resultados de carreira do aprendiz
26%
comecei uma nova carreira após concluir estes cursos
30%
consegui um benefício significativo de carreira com este curso
13%
recebi um aumento ou promoção
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível intermediário
Aprox. 31 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Alemão, Russo, Inglês, Espanhol
O que você vai aprender
Understand techniques such as lambdas and manipulating csv files
Describe common Python functionality and features used for data science
Query DataFrame structures for cleaning and processing
Explain distributions, sampling, and t-tests
Habilidades que você terá
Python ProgrammingNumpyPandasData Cleansing
Resultados de carreira do aprendiz
26%
comecei uma nova carreira após concluir estes cursos
30%
consegui um benefício significativo de carreira com este curso
13%
recebi um aumento ou promoção
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Nível intermediário
Aprox. 31 horas para completar
Inglês
Legendas: Árabe, Francês, Portuguese (European), Italiano, Vietnamita, Alemão, Russo, Inglês, Espanhol
oferecido por
Universidade de Michigan
The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
Programa - O que você aprenderá com este curso
12 horas para concluir
Fundamentals of Data Manipulation with Python
In this week you'll get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course syllabus, and you can find more information about the Jupyter Notebooks on our Course Resources page.
12 horas para concluir
12 vídeos (Total 111 mín.), 6 leituras, 2 testes
6 horas para concluir
Basic Data Processing with Pandas
In this week of the course you'll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing -- pandas. You'll learn how to read in data into DataFrame structures, how to query these structures, and the details about such structures are indexed.
6 horas para concluir
9 vídeos (Total 89 mín.), 1 leitura, 2 testes
7 horas para concluir
More Data Processing with Pandas
In this week you'll deepen your understanding of the python pandas library by learning how to merge DataFrames, generate summary tables, group data into logical pieces, and manipulate dates. We'll also refresh your understanding of scales of data, and discuss issues with creating metrics for analysis. The week ends with a more significant programming assignment.
7 horas para concluir
6 vídeos (Total 84 mín.), 1 leitura, 2 testes
6 horas para concluir
Answering Questions with Messy Data
In this week of the course you'll be introduced to a variety of statistical techniques such a distributions, sampling and t-tests. The week ends with two discussions of science and the rise of the fourth paradigm -- data driven discovery.
6 horas para concluir
2 vídeos (Total 20 mín.), 5 leituras, 2 testes
Avaliações
Principais avaliações do INTRODUCTION TO DATA SCIENCE IN PYTHON
por PS11 de Ago de 2020
Assignments are way tougher than what is taught in the class, but they are challenging and the help in discussion forums is speechless. Without that, completion of assignments will take too much time.
por PK9 de Mai de 2020
The course had helped in understanding the concepts of NumPy and pandas. The assignments were so helpful to apply these concepts which provide an in-depth understanding of the Numpy as well as pandans
por SI15 de Mar de 2018
overall the good introductory course of python for data science but i feel it should have covered the basics in more details .specially for the ones who do not have any prior programming background .
por AN15 de Ago de 2020
I found this course appealing because it was more practical based.it helped me alot in getting hands on experience and most of all I have learned how to solve real world problem with python libraries
Sobre Programa de cursos integrados Ciência de dados aplicada com Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.
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